Battery-powered electronic devices have become ubiquitous in modern society. The recent rapid expansion of the use of portable devices, electrically powered motors and the like has created a strong demand for fast deployment of battery technologies. The design of a battery-powered device requires many battery-management features, including charge control, battery-capacity monitoring, remaining run-time information, charge-cycle counting, and so on. Generally speaking, the basic task of a Battery Management System (BMS) is to ensure that optimum use is made of the energy inside the battery powering the portable product and that the risk of damage to the battery or surrounding circuitry is prevented. This may be achieved by monitoring and controlling the battery's charging and discharging process. One area where improvements are needed is in the area of battery failure detection.
Certain batteries, like lithium ion batteries (LIBs) have high specific energy and energy density among the different battery technologies, which makes them the ideal energy storage systems for electric vehicles (EVs) and hybrid electric vehicles (HEVs). To fully utilize LIBs in EV/HEVs, operation under 100% depth of discharge (DOD) is desired in order to achieve the highest utilization of their capacity and, consequently, their driving range (mile/charge cycle) and fuel economy (mile/gal). However, the current application of LIBs as energy storage systems in EVs and HEVs has not taken full advantage of their high specific energy and energy density because current EVs and HEVs operate between only 25 and 50% of the battery pack's capacity. This conservative operation (25-50%) provides a protection buffer to avoid unwanted cell degradation. However, such a buffer directly results in the reduction of the specific energy and energy density of a battery pack, and in the worst case, leads to an oversized battery system by as much as a factor of four and, consequently, a significant cost increase. Hence, operating the battery packs under/close to 100% capacity will take full advantage of the high specific energy and energy density of the LIBs and lead to the significant reduction of the cost of the battery pack in vehicle (EVs and HEVs) and other applications (stationary energy storage), which, in turn, will overcome the barriers for mass-market adoption of HEVs/EVs.
The high specific energy and energy density of LIBs are their great advantages for application in transportation. However, high energy density means packing a huge amount of energy in small volumes, which poses a safety hazard, in particular when high power is demanded for the LIB cells (i.e. acceleration of vehicle). High power usually requires high current, which in turn, causes excessive Ohmic heat generation inside a LIB cell and may cause fire or explosion. Even under the normal operating conditions, some LIB cells failed without any signs and caused significant loss or damage to the systems. The safety of LIBs is also critical for EV and HEV market adoption, as the premature failures in automotive batteries have already led to significant consumer dissatisfaction and cost hundreds of millions of dollars to consumer battery manufacturers in recalls and litigation. The failure of LIBs has a tremendous impact on EV and HEV market adoption and directly affects the cost and risk of deployment. Thus, a battery management system (BMS) with a function that can monitor the state of health of the LIB cells and predict incoming failure is needed
More specifically, there is a need to determine battery failure in advance of an actual failure. An online monitoring system is needed that is capable of both detecting internal cell faults and issuing an accurate and reliable warning for incoming failure of cells/modules in a battery pack under certain operating conditions and driving cycles. The major challenges for such a system lie in the uncertainty (random occurrence) and unobvious signals associated with failure. Current empirical trial-error approaches often result in detection with very low sensitivity and specificity due to the lack of a fundamental understanding of the faults.